Psychology, 2010, 1, 96-105
doi:10.4236/psych.2010.12013 Published Online June 2010 (http://www.SciRP.org/journal/psych)
When the Need for Cognitive Structure does not
Cause Heuristic Thinking: The Moderating Effect
of the Perceived Ability to Achieve Cognitive
Structure
Yoram Bar-Tal
Department of Nursing, School of Health Professions, Tel Aviv University, Tel Aviv, Israel.
Email: [email protected]
Received January 10th, 2010; revised April 30th, 2010; accepted May 4th, 2010.
ABSTRACT
This research explores the hypothesis that the relationship between need for cognitive structure (NCS) and the use of
cognitive biases is moderated by the perceived ability to achieve cognitive structure (AACS). NCS is defined as the extent of preference to use cognitive structuring vs. piecemeal processing as a means to achieve certainty. AACS refers to
the extent to which individuals believe that they are able to use information processing processes (cognitive structuring
or piecemeal) that are consistent with their level of NCS. To examine this hypothesis, Study 1 explored the effect of the
NCS by AACS interaction on the use of confirmation bias. Study 2, demonstrated this effect on the use of framing heuristic. The results of the two studies confirm the hypothesis.
Keywords: Need for Cognitive Structure, Ability to Achieve Cognitive Structure, Confirmation Bias, Framing Heuristics
1. Introduction
The idea that human information processing can be characterized by shortcuts which, although normally efficient
and powerful, may lead to biases or errors that systematically deviate from some accepted norm or standard,
dominates the study of cognitive and social psychology
in the last two decades. These shortcuts include a variety
of phenomena such as framing, causal schemata and confirmation bias. Inherent in the explanations of these phenomena is the idea that such shortcuts serve as general
simplifying strategies for complex cognitive tasks and
enable people to make inferences from and predictions on
the basis of such scanty and unreliable data as are available. These cognitive biases are often said to originate in
the limitations of otherwise reasonable informationprocessors. There are, however, indications that these
shortcuts are also related to more stable, trait-like characteristics. For example, it has been reported that the use
of heuristics in negotiation was moderated by the need
for cognitive closure [1,2]. Another study found relationships between Openness and judgmental accuracy [3].
The present paper centers on the need for cognitive structure (NCS) as the motivation force that may explain the
occurrence of these cognitive shortcuts.
Copyright © 2010 SciRes.
Cognitive structuring has frequently been regarded as
the most efficient way of making sense of the world.
Cognitive structuring can be defined as “the creation and
use of abstract mental representations (e.g., schemata,
prototypes, scripts, and stereotypes)-representations that
are simplified generalizations of previous experiences”
[4]. Cognitive structuring fulfills many functions in human information processing, such as the selection of information, avoidance of inconsistent information, or specific attendance to relevant information all of which are
functional in achieving certainty. Finally, cognitive structuring may facilitate achieving certainty by adding previously stored information concerning the validity of the
inference [5]. All these characteristics of cognitive structuring combine to offer the most efficient and relatively
effortless way of gaining a sense of certainty and control
over the situation [6].
While cognitive biases and heuristics are often portrayed as resulting from human cognitive shortcomings,
so that the effects of motivational factors on them are not
often explored, there is a wide body of research examining the effect of motivational factors on cognitive structuring. The motive that suggested to affect most information-processing behavior is the NCS. Thus, if the mechaPSYCH
When the Need for Cognitive Structure does not Cause Heuristic Thinking: The Moderating
Effect of the Perceived Ability to Achieve Cognitive Structure
nism that explains heuristics and cognitive biases is cognitive structuring, individual differences in NCS are expected to be associated with the extent of use of these
biases [7].
1.1 The Need for Cognitive Structure
NCS is presently defined as the extent of preference to
use cognitive structuring as a means to achieve certainty.
NCS has long been at the center of attention in psychological research [4,6,8,9].
This conception shares the assumption that the cognitive processes used by high-NCS individuals to reduce
uncertainty are “category based” [10,11], non-systematic
and heuristic. They prefer to use holistic and rapid processing, crudely differentiated categories black-and-white
type solutions and over-simplified dichotomizations. Fiske
[12] suggested that cognitive structuring is the cognitively easier default option when there is no reason to
discredit the categorization.
In contrast, low-NCS individuals are believed to prefer
to reduce uncertainty using “piecemeal” or “systematic
processing”, which are manifested in vigilant behavior,
based on a systematic and effortful search for relevant information, its evaluation and unbiased integration [13,14].
It is important to note that NCS is often conceptualized as
a dimension, which, at its high pole, predisposes individuals to use cognitive structuring to achieve certainty.
At its low pole, however, it is not associated with indifference or low motivation to achieve certainty, but with a
high tendency toward piecemeal processes [9].
Bar-Tal [15,16], however, argued that people may not
only differ in their need for cognitive structure but also in
their perceived ability to achieve cognitive structure (AA
CS), which is orthogonal to the need. Thus, the fact that
some people prefer to reduce their uncertainty by cognitive structuring does not mean that they believe that they
are able to do so. Similarly, other people’s wish to reduce
their uncertainty by means of piecemeal processes does
not imply that they expect themselves to be able to do so.
That is, according to Bar-Tal, AACS moderates the NCScognitive structuring relationship.
1.2 The Perceived Ability to Achieve Cognitive
Structure
AACS refers to the extent to which individuals believe
that they are able to employ information processing processes (cognitive structuring or piecemeal) that are consistent with their level of NCS. That is, in case of high
need of cognitive structure: 1) to avoid information that
either cannot be categorized or clashes with their existing
knowledge, and/or 2) to organize their knowledge to fit
an already existing cognitive structure. In the case of low
NCS this implies the extent to which they believe that
they are able to actively and systematically comprehend,
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97
evaluate and integrate all useful information.
This conceptualization suggests that for high-AACS
people, low NCS will probably be associated with individuating process, and high NCS with cognitive structuring. In contrast, for low-AACS people, low NCS implies
that they do not expect themselves to be able to achieve
certainty using piecemeal processing. Therefore they will
revert to low piecemeal, effortless processing. This postulate is consistent with Chaiken, Giner-Sorolla and Chen
[17] who suggest that accuracy motivation (low NCS)
does not always lead to systematic processing (piecemeal
process) since the latter can only take place if there is an
adequate capacity to process information. Chaiken., et al.
[17] furthermore suggest that when systematic processing
is difficult or impossible, an accuracy-motivated person
may have no choice but to base a decision on the best
rule of thumb available. The present model, however,
suggests that the perception of inability is sufficient to
explain the tendency to avoid systematic processing.
Low-AACS/high-NCS individuals, who prefer to use
cognitive structuring but do not expect themselves to be
able to do so, settle for more effortful processes. According to the present model, a state in which a person with a
high need for structure feels that he/she lacks or is unable
to use the structure that would enable him/her to organize
the available information, causes less efficient and more
effortful individuating processing. The idea that high
NCS may, under certain circumstances, be connected to
intensive bottom-up vigilant information search, rather
than the more predictable effortless “category based”,
non-systematic, is consistent with Janis and Mann’s [14]
description of the hyper-vigilant decision-maker, accordingly, hyper-vigilance is associated with indecisiveness,
over-alertness and the uncontrollable search for additional information. Bar-Tal [15] proposed that this behavior pattern stem from these people’s wish to reach an
unqualified decision (high NCS), and their perceived inability to achieve the desired certainty by means of cognitive structuring. Note that while low-AACS/high-NCS people do not use cognitive structuring to achieve certainty,
it does not mean that they use high piecemeal. Bar-Tal,
Kishon-Rabin and Tabak, [16] suggested that there are
differences between high piecemeal and hyper vigilance.
For example, while high piecemeal is associated with a
sensitivity to all relevant (hypothesis consistent and inconsistent information) hyper vigilance is associated in
addition, with sensitivity to hypothesis irrelevant information.
Recently Bar-Tal and his colleagues [15,16,18,19] provided empirical evidence in support of the notion of personal difference in AACS. Bar-Tal., et al. [16] also
demonstrated the moderating effect of AACS on the relationship between NCS and cognitive structuring by using
crude generalization and simplification as indices of cog-
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When the Need for Cognitive Structure does not Cause Heuristic Thinking: The Moderating
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nitive structuring. It has to be noted that in these studies,
the moderating effect of AACS on the relationship between NCS and cognitive structuring was demonstrated
by means of a variety of operationalizations of NCS including Need for Cognition scale [20].
When applying the idea that cognitive structuring is
affected by the interaction between NCS and AACS to
the notion that cognitive structuring is the basic mechanism enabling cognitive biases and heuristics [7,21], one
may hypothesize that the interaction will affect the use of
biases and heuristics. The present paper consists of two
studies that examine this hypothesis. Study 1 considers
how the interaction between NCS and AACS affects participants’ use of confirmation bias. Study 2 explores the
interaction effect on the use of framing heuristic. We
hypothesize that for low-AACS participants, an increase
of NCS will be associated with a lower level of confirmation-bias and heuristic use. In contrast, for high-AACS
participants, the increase of NCS will be associated with
increased of confirmation-bias and heuristic use.
2. Study 1
One of the simplification strategies people use when testing
a hypothesis is the confirmation bias. This bias is defined as
the tendency to seek only corroborating evidence [22], and
several types of it have been identified [23,24]. The
present research centers on only one of them, namely, the
tendency to avoid the examination of rival hypotheses.1
Baron., et al. [23] suggested that errors in hypothesis
testing, such as those evident in confirmation bias, can be
conceptualized as heuristics which come reasonably close,
without actually calculating, to the normative model (as
recommended by Popper [25] and others). The phenolmenon of confirmation strategies was validated in numerous studies [23,26]. Other researchers, however, have
claimed that people use a diagnostic strategy rather than a
confirmation strategy in hypothesis testing [24,27,28] so
that it can be concluded that people are capable of using
both confirmatory as well as diagnostic strategies. It has
also been suggested that the choice of confirmatory or
diagnostic strategy depends on the nature of the task and
of the instruction presented to the participants. Skov and
Sherman [28], for example, suggested that asking participants about the utility of various kinds of available information focuses them on the diagnosticity of that information. Also, Devine, Hirt and Gehrke [27] noted that
presentation of equally diagnostic hypothesis-true and
alternative-true questions leads participants to a preference for hypothesis true question.
Replacing confirmation bias with a diagnostic strategy
1
Baron, Beattie and Hershey [23] suggested three interrelated biases in
hypothesis testing (congruence bias, information bias and certainty bias),
of which congruence bias corresponds to the type of confirmation bias
the present study centers on.
Copyright © 2010 SciRes.
is of particular relevance in the case of medical diagnosis.
In the medical profession diagnostic strategy is termed
differential diagnosis (DD). The idea is that even when a
physician, or a nurse, finds that symptoms A, B and C,
which are very common in disease X, are present, he/she
cannot therefore safely infer that the patient suffers from
disease X since A, B, and C may also be common in
disease Y. DD requires the search for symptoms D and E
that are common in Y but do not characterize X. Only
when symptoms A, B and C are present and D and E
have not been found, the physician or nurse may be
certain in his/her diagnosis that the patient suffers from X
[29]. It has to be noticed however, that confirmation
strategy can reveal certain errors in the hypothesis and
does not necessarily lead to a mistaken conclusion: even
searching only for symptoms A, B and C (a confirmatory
strategy) may help to falsify the hypothesis if one or
more of the symptoms is missing. A truly diagnostic
strategy (examination of alternative explanations), however,
would increase the validity of the inference by accounting
for both necessary and sufficient conditions [26].
In the present context, people who use confirmation
bias are driven by the need to achieve certainty in the
validity of the hypothesis under consideration in an efficient
and easy way. Alternative hypotheses may not only prolong the validation process and make it more effortful, but
may in addition leave the individual uncertain regarding
the validity of any of the hypotheses. That is, refutation
of the original hypothesis by means of showing the
feasibility of an alternative hypothesis does not prove the
validity of the alternative. Achieving certainty in the
validity of the alternative hypothesis would require examining it, in turn, against its alternative hypotheses.
Therefore, a truly diagnostic strategy may not answer the
need of those who are motivated to achieve certainty in
an easy and fast way (high NCS). It is thus possible to
suggest that confirmation bias is the result of a cognitive
structuring process that allows people to achieve certainty
with a low expenditure of effort by attending mainly to
schema-consistent information while ignoring schemainconsistent or irrelevant information. Our hypothesis is
that for low-AACS participants, the higher their NCS the
more they will tend to use an individuating process and
search for diagnostic (DD) information. In contrast, for
participants with high AACS, the higher their NCS, the
greater will be their use of schematic and heuristic
processes and, therefore, the greater will be their use of
confirmation bias (ignoring the diagnostic information).
2.1 Method
2.1.1 Participants
Participants were 55 registered nurses working in a hospital in Israel. Their mean age was 38.82 (sd = 8.44) and
their average tenure 15.52 years (sd = 8.44).
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2.1.2 Measures
AACS Scale. The measure of AACS was carried out with
a 24-item questionnaire. The items were chosen to represent manifestations of ease or difficulty in using cognitive structure (e.g., “Usually, I don’t have afterthoughts
upon making a decision”; “Even when I am really bothered by a decision I should make, I still find it hard to
make up my mind and free myself from the hassle”, respectively), or ease or difficulty in using piecemeal processes (e.g., “Usually I see to it that my work is carefully
planned and well organized”; “Even if I make notes of
things I have to do, it is hard for me to act upon them”,
respectively). In terms of construct validity of the AACS
Scale, a high correlation between the R-S Scale [30] and
AACS Scale was found (r = –0.56, p < 0.01). High score
on the Scale represents high sensitivity and an inability to
filter out schema (self-schemata) incongruent contents
that is accompanied by motivation to maintain positive
self-esteem and ego integrity. The R-S scale does not
only represent inability to structure ego relevant contents.
Hock, Krohne and Kaiser [31] argued that sensitizers do
not tolerate uncertainty in general (high NCS) while being constantly and extensively preoccupied with enhanced information search. Therefore, a high R-S score
corresponds to low AACS. Also, since AACS represents
mastery of using the desired mode of information processing, and since self-efficacy should be strongly related
to self-esteem, a positive relationship can be expected
between AACS and self-esteem. Using Rosenberg’s SelfEsteem Scale [32], such a relationship was indeed found
(r = 0.52, p < 0.01) in a sample of students. In addition,
the AACS Scale was found to significantly correlate (r =
0.24, p < 0.05) with Cacioppo and Petty’s Need For Cognition Scale [20]. In general, people with a high need for
cognition prefer piecemeal processing and expend more
effort in processing information [33,34]. Finally, the
AACS Scale correlates negatively (r = –0.41, p < 0.01)
with the Dysfunctional Impulsivity Scale [35]. Dickman
defined impulsivity as the tendency to deliberate less than
most people of equal ability before taking action; impulsivity was divided into functional and dysfunctional types.
Functional impulsivity is the tendency to act with relatively little forethought when rapid response is required
and/or there is little cost of error. That is, functional impulsivity can be viewed as the tendency to use cognitive
structuring when this is the required process. Dysfunctional impulsivity was defined as the tendency to act with
less forethought than most people of equal ability, with
this tendency being a source of difficulty. Dysfunctional
impulsivity can therefore be viewed as the tendency to
use cognitive structuring when piecemeal process is required.2 Thus, to return to the found correlation between
AACS Scale and the Dysfunctional Impulsivity Scale, a
negative correlation indicates that the AACS Scale also
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measures the perceived ability to avoid cognitive structuring when piecemeal process required.3,4 Moreover, the
notion that the AACS Scale represents the ability to use
both piecemeal process as well as cognitive structuring
when desired, was validated in another sample of students, where each functional and dysfunctional impulsivity Scale was found to made a significant contribution to
the explanation of AACS, with a multiple R of 0.57.
The test-retest correlation (with an interval of five
weeks between measurements) was .86. Responses to the
24 items were on a 6-point Scale ranging from “Completely disagree” (1) to “{Completely agree” (6). The
composite AACS Scale score was the mean of responses
to the 24 items (Cronbach’s alpha = 0.82).
NCS Scale. NCS was measured by a 20-item questionnaire, with responses on a 6-point scale ranging from
“Completely disagree” (1) to “Completely agree” (6).
Items of the NCS were chosen to reflect specific personal
preferences (e.g., “I am very annoyed when something
unexpected disrupts my daily routine”; “I prefer things to
be predictable and certain”), as well as general attitudes
and values indicating preference for the unequivocal and
absolute (e.g., “I don’t like modern paintings in which I
don’t know what the painter meant”; “In order to get a
good dish it is absolutely essential to follow the recipe
exactly”). Items were selected so that they will reflect
only motivation and preference, and not actual behavior,
since the latter represents ability as well as need.5 The
composite score was the mean of responses to the 20
items (Cronbach’s alpha = 0.86). The test-retest correlation (with an interval of five weeks between measurements) was 0.85. In terms of construct validity, the NCS
Scale was found to be positively correlated (r = 0.43)
with Rokeach’s dogmatism Scale [36], (r = 0.45) with the
personal-need-for-structure Scale [4], and (r = 0.68) with
the need for closure Scale [37], which all represent constructs similar to NCS. Finally, given that the NCS Scale
represents a dimension both of whose ends are related to
high need for certainty (though each is achieved differently), and that need for certainty, in turn, should be correlated with need for control, a curvilinear relationship
between desire for control and NCS could be predicted.
2
For the relationship between impulsivity and cognitive structuring see
Dickman [60] and Dickman and Meyer [61].
3
Support for the notion that cognitive structuring is not the preferred
method of high dysfunctional impulsives is its negative correlation (r =
–0.27, p < 0.01) with NCS.
4
It is interesting to note that the negative correlation between DI and
AACS exists even after controlling for level of self-esteem (r = –0.35,
p < 0.01). That is, the negative correlation between AACS and DI
cannot be explained by the positive correlation between AACS and
self- esteem.
5
This was the original reason for constructing a new scale rather than
using one of the already existing one. Only in a latter stage it was
established that other existing scales that measure the same construct
are not correlated with AACS.
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Indeed a significant curvilinear (r = 0.25, p < 0.05), but
not linear (r = 0.06, p = ns), relationship was found, between Burger and Cooper’s [38] desire for control Scale
and NCS Scales.
Stimuli Participants were presented with two written
scenarios describing patients admitted to the emergency
ward: one, a male with a suspected cerebrovascular accident (CVA), the other, a woman with abdominal pains
indicating appendicitis. Each scenario was followed by
15 items suggesting possible tests needed to be done to
achieve certainty in the diagnosis: positive answers on
five of these tests confirmed the hypothesis (given diagnosis), five other items were diagnostic (positive answers
pointed at the possibility that the differential diagnosis
was correct) and five irrelevant. For the CVA scenario, a
typical consistent item was “Checking for hemiparesis”, a
diagnostic item was “Is the patient taking medicines that
are known to cause mental confusion?”, while an irrelevant item was, for instance: “Did the patient have rubella
as a child?” Similarly, a consistent item with regard to the
appendicitis scenario was “Is there a difference between
the rectal and oral temperature?” A representative diagnostic item was “Is the patient known to suffer from gallstones?”, while an irrelevant item was “Does the patient
suffer from asthma?” All items were validated by a panel
of two practitioners and three registered nurses.
The participants were asked to read each of the 15
suggested diagnostic questions that came with the scenarios, and to answer the following question: “To what
extent can this test/question help you to decide whether
the diagnosis is correct?" The reliability of the 10 consistent items was 0.81, that of the diagnostic ones was 0.70
and that of the irrelevant information was 0.77.
2.1.3 Procedure
Participants were approached at their working places by
the experimenter who presented herself as a MA Nursing
student and asked them to participate in a decision-making
study. After completing the two tasks, they were requested to complete the AACS and NCS Scales. Upon
completion participants were debriefed.
2.2 Results and Discussion
Since our main hypothesis relates to the moderating effect of the AACS × NCS interaction on confirmation bias,
it is necessary first of all to establish the existence of
confirmation bias. For this purpose, the mean ratings of
consistent, irrelevant and diagnostic items were compared
using a one-way ANOVA with repeated measure test.
The analysis yielded a highly significant result (F(2,108) =
869.20, p < 0.01), with the mean of relevant items (M =
5.67) being higher than that of the diagnostic items (M =
3.95), while the latter mean was higher than that of the
irrelevant items (M = 1.52). The a posteriori Bonferoni
Copyright © 2010 SciRes.
Table 1. Correlation among study 1 variables
1
2
3
1. AACS
2. NCS
18
3. confirmation bias
–0.05
05
mean
3.83
4.10
1.72
66
70
86
sd
tests for dependent measures show that all the three
measures differed significantly from each other. Thus,
while participants clearly underestimated the utility of
diagnostic information relative to hypothesis-consistent
information, they nevertheless noticed that the diagnostic information was more informative than the irrelevant in
formation. Hence, the data supported a predominant
hy-pothesis-confirmation strategy, and a less strong, but
nonetheless significant tendency to acknowledge the merit
of diagnostic information. It is interesting to note that
these findings were obtained in spite of the fact that the
participants were requested to judge the utility of the test
items for achieving certainty regarding the validity of the
hypothesis. That is, Skov and Sherman’s [28] suggestion
that such a method leads to a subject’s choice of diagnostic strategy is not supported by the present results.
To test the hypothesis, a confirmation bias index was
constructed by subtracting the mean diagnostic items
from the consistent items. Consequently, a higher score
on the index represents greater extent of deviation from a
diagnostic strategy. Next, the correlations among the
study variables were calculated (see Table 1). Finally, a
hierarchical regression was performed in which the two
standardized main effects (AACS and NCS) where entered in the first step and the interaction term (AACS ×
NCS) was introduced in the second step. Following the
suggestion of Dunlap and Kemery [39] concerning the
reduction of multicollinearity, all variables were standardized before the respective cross-products were computed. The regression analysis as a whole was significant
(F(3,51) = 3.22, p < 0.05), and only the interaction yielded a
significant effect explaining 15% of the variance (t = 2.98,
p < 0.01). The final equation for confirmation bias is
Y’ = 1.66 – 0.01*A – 0.08*B + 0.34*AB,
with A standing for NCS and B for AACS. To interpret
the source of the interactions, regression lines of confirmation bias on NCS were calculated separately for high
and low AACS according to one standard deviation below and above the mean. Since analyses were based on
the z-scores of the independent variables, values were –1
and 1. Regression coefficients (b) were calculated using
the equation obtained in the final step; the b of NCS was
added to that of the interaction term after the latter was
multiplied by either –1 or 1 [40].
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When the Need for Cognitive Structure does not Cause Heuristic Thinking: The Moderating
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In line with our hypothesis, the slope for the lowAACS participants was negative (b = –0.35), while it was
positive for high AACS (b = 0.33). That is, for lowAACS participants, higher NCS was associated with
lower confirmation bias and higher use of diagnostic
strategy. In contrast, for high-AACS participants higher
NCS was associated with higher confirmation bias. Thus,
this study demonstrated that the relationship between
NCS and the use of cognitive biases is moderated by
level of AACS. Finally, the findings show, in contrast to
previous research, that the choice of confirmatory or diagnostic strategy is not determined by “cold” cognitive
factors only, as mentioned earlier, but also by participants’ epistemic motivation and their efficacy to satisfy
this motivation.
3. Study 2
It is widely recognized that when making everyday
judgments in uncertain situations people will very seldom
use exhaustive statistical analysis to figure out the best
choice. Rather, they often rely more pragmatically on
simplifying judgmental strategies. These strategies, commonly termed heuristics, provide decision-making shortcuts as an alternative to the elaborate, more rational
processes [41,44]. As it their function to achieve certainty
in the easiest and quickest way by relying mainly on the
most salient information, heuristics can be considered as
manifestations of cognitive structuring processes. Indeed,
Kruglanski and Freund [45] demonstrated that when people were motivated to achieve cognitive structure they
showed a greater tendency to use the numerical anchoring heuristic [44]. Also, Henderson and Peterson [21]
suggested that at least some of the framing heuristic scenarios are better explained by categorization processes.
Framing is one of the most commonly cited heuristic
strategies. Tversky and Kahneman [46,47] proposed the
concept of decision frame. When decision options are
phrased in terms of gains, most people choose the risk
averse option. But when options are phrased negatively in
terms of losses, most people choose the risky option. This
preference reversal relates to the alternative framing
which causes people to view the outcomes as gains in the
positive frame and as losses in the negative frame [46,47].
To account for the deviation of these results from the
predictions of expected utility theory, Kahneman and
Tversky [48] suggested the prospect theory. In prospect
theory, the decision making process is divided into two
phases: an editing phase in which the decision problem is
edited into a simpler representation in order to make the
second phase easier for the decision maker. The framing
effect is mainly created in this second phase, which consists of an evaluation of the framed course of action for
the final choice. Kahneman and Tversky [49] concluded
that the use of framing heuristic is both pervasive and
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101
robust to the extent that it resembles perceptual illusions
more than conceptual errors. In spite of ample research
supporting Kahneman and Tversky’s findings, recent
research challenges their conviction by showing that the
phenomenon is much more restricted than they suggested
[50-53]. Fagley and Miller [51], for example, found no
framing effect and Bier and Connell [50] even described
a reversed framing effect. Wang and Johnston [53] demonstrated that the effect appeared only when describing a
large-group context but not in a small group. Takemura
[54] found that framing effect tends to disappear when
participants are requested to justify their choice, to think
about it, or when they have a long time for solving the
problem. Finally, there are indications that framing effect
is strongly affected by individual differences. Smith and
Levin [55] established that framing effects are obtained
for participants low in need for cognition but not for participants scored high on this Scale. Similarly, Shiloh,
Salton and Sharabi [56] demonstrated that rational and
intuitive thinking styles [57] are associated with framing
effect.
One of Tversky and Kahneman’s most famous examples of framing is the following “Asian disease” example
[46].
Imagine that the United States is preparing for the outbreak of an unusual Asian disease expected to kill 600
people. Two alternative programs to combat the disease
have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows (the positive frame):
If Program A is adopted 200 people will be saved.
If Program B is adopted, there is a 1/3 probability that
600 people will be saved and a 2/3 probability that no
people will be saved. Which of the two programs
Would you favor?
The other formulation of the programs (negative framing) included the following two options:
If Program C is adopted, 400 people will die.
If Program D is adopted, there is 1/3 probability that
nobody will die and
a 2/3 probability that 600 people will die (p. 453).
Tversky and Kahneman [46] indicated that the two
versions induced different frames and therefore caused
participants to adopt different decisions. Participants preferred program A over B (the risk averse response), but
program D over program C (the risk seeking response).
If our assumption that framing heuristic is a manifestation of cognitive structuring is correct, how can this phenomenon be attributed to simplification processes that are
consistent with cognitive structuring? The explanation
may be as follows: Participants seek the most positive or
least negative consequence. However, when motivated to
use cognitive structuring process, participants are also
motivated to seek the simpler choice. Thus, in the posi-
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tively framed scenario, participants prefer program A
because it is simpler (only positive consequences are
mentioned), in contrast to program B which details both
positive and negative consequences and requires calculations. In the negatively framed scenario participants
avoid program C because it is clearly negative and by
default (i.e., without considering it) they choose program
D. Otherwise, they scan program D, find the positive part
of it (nobody will die), and disregarding the other elements of the option, they prefer it to the clearly negative
option, program C. In line with this explanation, Kuhberger [52] demonstrated that the framing effect disappears when participants are presented with complete and
mixed programs, i.e., 200 will be saved and 400 will not
be saved, in program, A and 400 will die and 200 will not
die in program C. That is, when there is no clear-cut program which enables simplification, the framing effect is
not evident.
Since framing can be viewed as one of the manifestations of cognitive structuring processes, and since cognitive structuring is affected by the interaction between
NCS and AACS, the present study hypothesizes that the
use of framing will be moderated by the interaction between AACS and NCS. In other words, high-AACS participants will tend to use more framing the higher their
NCS. In contrast, low-AACS participants will manifest
negative correlation between their level of NCS and utilization of framing heuristic. The rationale for this hypothesis is that participants with high NCS will be more
motivated to use the heuristic; however, those with low
AACS will be able neither to sufficiently structure their
cognition, nor to avoid inconsistent information, nor,
indeed, to use heuristics for the sake of a quick and easy
decision process.
3.1 Method
3.1.1 Participants
Participants were 51 women and 42 men whose average
age was 39.12 (sd = 13.02) and whose average years of
formal education were 14.12 (sd = 2.70); all participants
agreed to participate in the study.
3.1.2 Measures
Need and ability to achieve cognitive structure. NCS and
AACS were measured by scales described in Study 1.
The reliability of AACS was 0.80 and that of NCS was
0.89.
Measure of framing effect. Participants were presented
with a modification of Tversky and Kahneman’s [46]
“plague problem” i.e., they were requested to imagine
that they returned a week ago from an exciting trip to the
Far East. The Health Department declared that their experience from previous years showed that annually, 1800
people of all those who travel to the Far East were inCopyright © 2010 SciRes.
fected by a certain virus which does not cause an immediate symptom or health problem. A proper diagnosis
enables cure of the disease. The Health Department announced that there are two kinds of tests to diagnose the
disease, but neither is fully reliable. For the first test there
is 1/3 probability that 1800 people will be diagnosed correctly and 2/3 probability that no one will be diagnosed
correctly. For the second test, 600 people are diagnosed
correctly. The introductory story of the negative version
was similar to that of the positive one, the only difference
being in the wording of the options, namely, in the first
test 1200 people would not be diagnosed correctly, and in
the second test, there was 1/3 probability that none would
be diagnosed incorrectly and 2/3 probability that 1800
people would not be diagnosed correctly. Having read
each of the two scenarios participants were requested to
rate the extent to which they would chose to be tested by
each of the tests, on 100 mm visual analog scales ranging
from “not all” (0) to “to a very large extent” (100). The
positive and negative versions were separated by a fiveminute distraction task. The order of the two scenarios
was counter balanced.
3.1.3 Procedure
Participants were told that the study examined various
aspects of decision-making and that their anonymity
would be preserved. Then they were requested to complete both versions of the framing scenarios as well as the
intervening distraction task. Finally, they answered the 44
items of the AACS and NCS questionnaires.
3.2 Results and Discussion
To examine the effectiveness of the framing manipulation,
a 2 × 2 within-subject ANOVA (gains vs. losses, and
risk-aversion vs. risk-taking) was performed. The analysis yielded only a significant interaction effect (F(1,92) =
29.31, p < 0.01). The a posteriori Tukey/b tests performed on the residuals [58] show that in problem 1
(framed as a gain), participants preferred the risk-aversion alternative (9.49) over the risk-taking one (–9.49). In
contrast, in the case of problem 2 participants preferred
the risk-taking option (9.49) over the risk aversion option
(–9.49). Hence, the successfulness of the manipulation of
the framing effect is highly evident.
To examine the study’s main hypothesis, a total framing score was calculated by summing of the preference
for risk-avoiding over risk-taking ratings in the first problem, and the preference for risk-taking over risk-avoiding
ratings in the second problem. Subsequently, the correlations among the study variables were calculated (see Table 2). Finally, a hierarchical regression analysis was
performed in whose first step the effects of AACS and
NCS were introduced, while the second examined the interaction effect. The analysis shows that the regression as
a whole was significant (F(3,85) = 3.00, p < 0.05), and only
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When the Need for Cognitive Structure does not Cause Heuristic Thinking: The Moderating
Effect of the Perceived Ability to Achieve Cognitive Structure
Table 2. Correlation among study 2 variables
1
2
3
1. AACS
2. NCS
3.framing
heuristic
mean
sd
–0.01
–0.02
15
3.73
4.36
37.96
65
83
67.61
the interaction yielded a significant effect, explaining 9%
of the variance (t = 2.65, p < 0.01). The final equation
for framing effect is
Y’ = 34.18 + 7.88 * A + 2.24 * B + 18.61 * AB
with A standing for NCS and B standing for AACS.
The examination of the source of the interaction was
performed as in Study 1.
In line with our hypothesis, while the slope of total
framing effect for the low-AACS participants was negative (b = –10.73), it was positive for high AACS (b =
26.49). Thus, as predicted, while high AACS participants
tended to use more framing heuristic the higher their level of NCS, low-AACS participants tended to use it less
the higher their NCS.
The results from this study contradict Kahneman and
Tversky’s [49] claim regarding the robustness of the
phenomenon of framing heuristic. If there are substantial
amounts of people who tend to avoid the use of framing,
it is not reasonable to view the framing effect as perceptual illusion. The present results cannot moreover be explained in terms of the prospect theory. Therefore, Tversky and Kahneman’s [46] conviction that the framing
phenomenon is best explained by prospect theory stands
challenged. This study joins the other studies, mentioned
earlier, that show the limitations of the phenomenon as
well as the limitations of prospect theory’s capacity in
explaining this phenomenon. In addition, this study contributes in that rather than emphasizing either cognitive
(mentioned above), or motivational factors [50,55,57]
that effect the framing phenomenon, it demonstrates that
framing, like other heuristic and cognitive biases, is influenced by a combination of motivational and cognitive
factors.
shows that while for high-AACS participants, the level of
NCS is positively associated with the use of framing heuristic, for low-AACS participants, the level of NCS is
negatively associated with framing heuristic.
The present results further validate our view that cognitive structuring, manifested in the present study by the
use of cognitive biases and stereotyping, is affected by
both NCS and AACS and not by NCS alone. This conclusion highlights the importance of distinguishing between the two constructs. In addition, the fact that in the
two different samples, the correlations between NCS and
AACS were found to be very low and non-significant not
only supports this conclusion but also indicates that the
two measures of AACS and NCS reflect different constructs.
The two studies are based on the assumption that the
basic mechanism underlying cognitive biases is similar to
that of motivational biases and that both of them are manifestations of cognitive structuring. From this point of
view, the present results validate Kruglanski and his colleagues’ [7,45,59] claim that both cognitive and motivational biases are manifestations of the same epistemic
motivational processes. Our present results however, depart from Kruglanski’s lay epistemology theory in one
important respect, namely that according to our conception people are not always able to adapt information
processing processes (cognitive structuring or piecemeal;
freezing or unfreezing, in lay epistemology terms) whenever they wish or need it.
To conclude, the present paper suggests that heuristic
thinking cannot be explained by the mere motivation for
simplified, relatively homogeneous, well-defined and distinct structures. Rather we suggest that the relation between this motivation and schematic thinking is moderated by the efficacy to achieve cognitive structure.
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